198 lines
6.2 KiB
Python
Executable File
198 lines
6.2 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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优化扩散模型使用示例
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演示如何使用优化后的IC版图扩散模型
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"""
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import os
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import sys
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import torch
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import subprocess
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from pathlib import Path
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def example_basic_training():
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"""基本训练示例"""
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print("=== 基本训练示例 ===")
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# 创建示例数据目录
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data_dir = "example_data/ic_layouts"
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output_dir = "example_outputs/basic_training"
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# 训练命令
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cmd = [
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sys.executable, "run_optimized_pipeline.py",
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"--data_dir", data_dir,
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"--output_dir", output_dir,
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"--epochs", 20, # 示例用较少轮数
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"--batch_size", 2,
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"--image_size", 256,
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"--num_samples", 10,
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"--create_sample_data", # 创建示例数据
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"--edge_condition",
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"--augment"
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]
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print(f"运行命令: {' '.join(map(str, cmd))}")
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print("注意：这将创建示例数据并开始训练")
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# 实际运行时取消注释
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# subprocess.run(cmd)
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def example_advanced_training():
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"""高级训练示例"""
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print("\n=== 高级训练示例 ===")
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cmd = [
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sys.executable, "train_optimized.py",
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"--data_dir", "data/high_quality_ic_layouts",
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"--output_dir", "models/advanced_diffusion",
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"--image_size", 512, # 更高分辨率
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"--batch_size", 8,
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"--epochs", 200,
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"--lr", 5e-5, # 更低学习率
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"--manhattan_weight", 0.15, # 更强的几何约束
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"--edge_condition",
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"--augment",
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"--schedule_type", "cosine",
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"--save_interval", 5,
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"--sample_interval", 10
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]
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print(f"高级训练命令: {' '.join(map(str, cmd))}")
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def example_generation_only():
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"""仅生成示例"""
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print("\n=== 仅生成示例 ===")
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cmd = [
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sys.executable, "generate_optimized.py",
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"--checkpoint", "models/diffusion_optimized/best_model.pth",
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"--output_dir", "generated_samples/high_quality",
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"--num_samples", 100,
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"--num_steps", 30, # 更快采样
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"--use_ddim",
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"--batch_size", 16,
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"--use_post_process",
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"--post_process_threshold", 0.45
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]
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print(f"生成命令: {' '.join(map(str, cmd))}")
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def example_custom_parameters():
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"""自定义参数示例"""
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print("\n=== 自定义参数示例 ===")
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# 针对特定需求的参数调整
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scenarios = {
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"高质量生成": {
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"description": "追求最高质量的生成结果",
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"params": {
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"--num_steps": 100,
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"--guidance_scale": 2.0,
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"--eta": 0.0, # 完全确定性
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"--use_post_process": True,
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"--post_process_threshold": 0.5
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}
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},
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"快速生成": {
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"description": "快速生成大量样本",
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"params": {
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"--num_steps": 20,
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"--batch_size": 32,
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"--eta": 0.3, # 增加随机性
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"--use_ddim": True
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}
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},
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"几何约束严格": {
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"description": "严格要求曼哈顿几何",
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"params": {
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"--manhattan_weight": 0.3, # 更强约束
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"--use_post_process": True,
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"--post_process_threshold": 0.4
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}
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}
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}
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for scenario_name, config in scenarios.items():
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print(f"\n{scenario_name}: {config['description']}")
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for param, value in config['params'].items():
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print(f" {param}: {value}")
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def example_integration():
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"""集成到现有管线示例"""
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print("\n=== 集成示例 ===")
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# 更新配置文件
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config_update = {
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"config_file": "configs/train_config.yaml",
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"updates": {
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"synthetic.enabled": True,
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"synthetic.ratio": 0.0,
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"synthetic.diffusion.enabled": True,
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"synthetic.diffusion.png_dir": "outputs/diffusion_optimized/generated",
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"synthetic.diffusion.ratio": 0.4,
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"synthetic.diffusion.model_checkpoint": "outputs/diffusion_optimized/model/best_model.pth"
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}
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}
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print("配置文件更新示例:")
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print(f"配置文件: {config_update['config_file']}")
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for key, value in config_update['updates'].items():
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print(f" {key}: {value}")
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integration_cmd = [
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sys.executable, "run_optimized_pipeline.py",
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"--data_dir", "data/training_layouts",
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"--output_dir", "outputs/integration",
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"--update_config", config_update["config_file"],
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"--diffusion_ratio", 0.4,
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"--epochs", 100,
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"--num_samples", 500
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]
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print(f"\n集成命令: {' '.join(map(str, integration_cmd))}")
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def show_tips():
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"""显示使用建议"""
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print("\n=== 使用建议 ===")
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tips = [
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"🎯 数据质量是关键：使用高质量、多样化的IC版图数据进行训练",
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"⚖️ 平衡约束：曼哈顿权重不宜过高（0.05-0.2），避免过度约束影响生成多样性",
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"🔄 迭代优化：根据生成结果调整损失函数权重和后处理参数",
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"📊 质量监控：定期检查生成样本的质量指标",
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"💾 定期保存：设置合理的保存间隔，避免训练中断导致损失",
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"🚀 性能优化：使用DDIM采样可以显著提高生成速度",
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"🔧 参数调优：根据具体任务需求调整模型参数"
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]
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for tip in tips:
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print(tip)
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def main():
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"""主函数"""
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print("IC版图扩散模型优化版本 - 使用示例")
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print("=" * 50)
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# 检查是否在正确的目录
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if not Path("ic_layout_diffusion_optimized.py").exists():
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print("错误：请在 tools/diffusion/ 目录下运行此脚本")
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sys.exit(1)
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# 显示示例
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example_basic_training()
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example_advanced_training()
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example_generation_only()
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example_custom_parameters()
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example_integration()
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show_tips()
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print("\n" + "=" * 50)
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print("运行示例：")
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print("1. 基本使用：python run_optimized_pipeline.py --data_dir data/ic_layouts --output_dir outputs")
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print("2. 查看完整参数：python train_optimized.py --help")
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print("3. 查看生成参数：python generate_optimized.py --help")
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print("4. 阅读详细文档：README_OPTIMIZED.md")
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if __name__ == "__main__":
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main() |